GPT-3 is everywhere. The AI tool has sent the internet into a frenzy, as users marvel at its accelerated text generation capabilities and fixate on its potential business use cases.
But what exactly is GPT-3, and how does it work? And, most importantly, is it worth all of the hype? Below, we answer all of your questions about the modern AI tool in this complete guide to GPT-3.
Table of Contents
What is GPT-3?
GPT-3 is a language model that can process and generate human-like text. The tool was developed by OpenAI, an AI research lab, and is currently available as an API.
GPT stands for generative pre-trained transformer. The “training” references the large compilation of text data the model used to learn about the human language.
During its creation process, GPT-3 digested billions of words to become well-versed in understanding human language, analyzing the meaning behind words, and generating language independently. GPT-3 is trained in many languages, not just English.
How does GPT-3 work?
Let’s backtrack a bit. To fully understand how GPT-3 works, it’s essential to understand what a language model is.
A language model uses probability to determine a sequence of words — as in guessing the next word or phrase in a sentence.
GPT-3 uses natural language processing (NLP), a function of artificial intelligence (AI). AI is the idea that computers can be programmed to complete human tasks. NLPs fall under the general AI umbrella and focus on the communication aspect of that programming, specifically between computers and humans.
When it comes to construction, GPT-3 is powered by four main models under Open AI. Each model is powered differently, and each offers different capabilities suitable for various tasks. These are the models:
- Davinci
- Curie
- Babbage
- Ada
This video offers a thorough explanation of how GPT-3 works compared to other language models.
GPT-3 is more powerful than the NLPs that came before it. GPT-3 contains 175 billion parameters which make it 10 times greater in size than previous processors.
Another element that makes GPT-3 different from other language models is its human-like accuracy. The NLPs that came before were more focused on fine-tuning and struggled with reading comprehension, filling in the blanks, and question answering.
GPT-3 has tackled all of these challenges to become the most powerful language processor to date.
Why is GPT-3 so powerful?
GPT-3 is a major development for modern technology and communication. Not only does it help facilitate communication between computers and humans, but it can also be used to improve a wide range of processes.
Here are a few benefits and use cases of GPT-3 in today’s context.
Text Generation
GPT-3 uses NLP not only to analyze and understand human text but also to create text that is human-like in response. This is the biggest takeaway and arguably the greatest use case for the AI tool, as it can be applied to various tasks.
Text generation can be useful for real-time communication, responding to prompts, and filling in the blanks, among other things.
Here are a few use cases for text generation using GPT-3:
- Customer service
- Virtual assistants
- Chatbots
- Content creation
- Language translation
For example, let’s say you want to improve your customer service support process. You can use GPT-3 to generate instant and human-like responses on behalf of your customer support team.
Because GPT-3 can quickly answer questions and fill in the blanks when needed, it can be used for real-time, back-and-forth messaging in customer service. It would also help reduce the response time, which customer support professionals know is critical.
Another valuable way to use GPT-3 is for content creation purposes. The AI can generate text for various content assets such as social media posts, blog posts, and even video scripts. The best part, again, is how quickly GPT-3 can produce content.
By taking advantage of GPT-3’s speed, brands and creators can cut significant time out of the content creation process, which is valuable when producing a substantial amount of content on a regular basis.
Adaptability
While it’s not perfect, GPT-3 has been highly trained in deep learning and can adapt to a wide range of tasks aside from text generation.
While it’s not perfect, GPT-3 has been highly trained in deep learning and can adapt to a wide range of tasks. For example, the tool can be used to generate simple code. This makes it powerful for creators and developers who want to integrate NLP into their applications but lack the right expertise.
Remember that generating more complex tasks like code is not GPT-3’s expertise. So while it can produce lines of code when prompted correctly, the code may need debugging to ensure it meets the intended functionality.
The key to getting the most out of this function is giving GPT-3 the right prompts to help program what it produces.
It’s also important to note that GPT-3 is not open source but can be accessed through an API. This makes it easy for developers to integrate it into existing applications. Developers can use GPT-3 to create NLP features in their applications without developing their own algorithms or models.
Time and Cost Savings
GPT-3 is fast. The speed at which it can generate text is incomparable.
For example, when used to fill in the blanks for prompts or to answer questions, GPT-3 can have a response ready in seconds.
While it may take longer for GPT-3 to handle more complex tasks, such as analyzing large datasets, the tool is still much faster than other processors — or humans, for that matter.
When used to supplement or support an organization’s current practices, GPT-3 can save time. And saving time helps save on costs, which is another important consideration for organizations of all sizes — from lean startups to enterprise-level companies.
With the time and cost savings from using a text generator like GPT-3, your organization can use those resources in more effective business areas that will drive impactful results.
GPT-3 Limitations
GPT-3 may be a valuable language processing tool, but it’s also important to consider its limitations before diving in. Here are a few ways GPT-3 may be limited in its functions when put into practice.
Bias
One of the biggest limitations of GPT-3 is that the information it generates or presents can be biased. This is because it reflects the data it was trained on.
So, if all of the data that was used to train it suggested that dogs are better than cats, then that bias would most likely appear in any text GPT-3 generates about dogs or cats. This is obviously just an example, but it represents a larger flaw in the design.
Bias can be harmful when it’s taken at face value. As with anything found or created on the internet, it’s always best to consider multiple sources of information before making a statement or taking action.
Memory
As human-like as it is, GPT-3 doesn’t have a long-term memory and can’t retain information from each interaction it has. It’s not designed to have ongoing conversations, which means it can’t be used for a continuous or evolving task.
For example, if you’re using GPT-3 to help form responses for your customer support team, it won’t have any memories of each interaction once it’s over. Each session would be considered independent, even if the same customer comes back the next day for support.
Full Context
At the end of the day, GPT-3 is not a human. While it can produce human-like text, it still lacks full context and natural common sense. As a result, it can generate text that doesn’t quite make sense. This is a common result when GPT-3 doesn’t have full context around a scenario.
To navigate this limitation, provide as many details as possible when prompting GPT-3 with a task. Doing so can help limit inaccurate or irrelevant statements. It’s also important to review any text GPT-3 generates to correct any inaccuracies before publishing it somewhere for an audience.
Getting Started
GPT-3 is a powerful language processor — probably the best one yet. It can help save time and resources by generating human-like text, filling in the blanks, and answering questions in seconds.
However, it comes with its limitations, which should be considered before using GPT-3 as a replacement for any one tool or practice.
To get the most out of GPT-3, consider its uses and limitations and experiment with how it can best support your needs.
